lets_plot.geom_vline

lets_plot.geom_vline(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, xintercept=None, **other_args)

Add a straight vertical line to the plot.

Parameters

  • mapping (FeatureSpec) – Set of aesthetic mappings created by aes() function. Aesthetic mappings describe the way that variables in the data are mapped to plot “aesthetics”.

  • data (dict or DataFrame) – The data to be displayed in this layer. If None, the default, the data is inherited from the plot data as specified in the call to ggplot.

  • stat (str, default=’identity’) – The statistical transformation to use on the data for this layer, as a string.

  • position (str or FeatureSpec) – Position adjustment, either as a string (‘identity’, ‘stack’, ‘dodge’, …), or the result of a call to a position adjustment function.

  • show_legend (bool, default=True) – False - do not show legend for this layer.

  • sampling (FeatureSpec) – Result of the call to the sampling_xxx() function. Value None (or ‘none’) will disable sampling for this layer.

  • tooltips (layer_tooltips) – Result of the call to the layer_tooltips() function. Specifies appearance, style and content.

  • xintercept (float) – The value of x at the point where the line crosses the x axis.

  • other_args – Other arguments passed on to the layer. These are often aesthetics settings used to set an aesthetic to a fixed value, like color=’red’, fill=’blue’, size=3 or shape=21. They may also be parameters to the paired geom/stat.

Returns

Geom object specification.

Return type

LayerSpec

Note

geom_hline() understands the following aesthetics mappings:

  • xintercept : line x-intercept.

  • alpha : transparency level of a layer. Understands numbers between 0 and 1.

  • color (colour) : color of a geometry lines. Can be continuous or discrete. For continuous value this will be a color gradient between two colors.

  • size : lines width. Defines line width.

  • linetype : type of the line. Codes and names: 0 = ‘blank’, 1 = ‘solid’, 2 = ‘dashed’, 3 = ‘dotted’, 4 = ‘dotdash’, 5 = ‘longdash’, 6 = ‘twodash’.

Examples

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from lets_plot import *
LetsPlot.setup_html()
ggplot() + geom_vline(xintercept=0)


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import numpy as np
import pandas as pd
from lets_plot import *
LetsPlot.setup_html()
n = 100
classes = ['a', 'b', 'c']
np.random.seed(42)
x = np.random.normal(size=n)
y = np.random.normal(size=n)
c = np.random.choice(classes, size=n)
df = pd.DataFrame({'x': x, 'y': y, 'c': c})
bounds_df = pd.DataFrame([(cl, df[df.c == cl].x.max()) for cl in classes], \
                         columns=['c', 'xmax'])
ggplot() + \
    geom_vline(aes(xintercept='xmax', color='c'), \
               data=bounds_df, size=.2, linetype='longdash') + \
    geom_point(aes(x='x', y='y', color='c'), data=df)